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Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2026 / Cilt: 25 Sayı: 1

FRUIT CLASSIFICATION WITH CONVOLUTION NEURAL NETWORK MODELS USING ILLUMINATION ATTRIBUTE

AYDINLATMA ÖZNİTELİĞİ KULLANILARAK EVRİŞİMSEL SİNİR AĞI MODELLERİ İLE MEYVE SINIFLANDIRMA

Birkan Büyükarıkan(SELÇUK ÜNİVERSİTESİ, SARAYÖNÜ MESLEK YÜKSEKOKULU, BİLGİSAYAR TEKNOLOJİLERİ BÖLÜMÜ)0000-0002-9703-9678
Erkan Ülker(KONYA TEKNİK ÜNİVERSİTESİ, MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0003-4393-9870
Pages
81–100
Publication Date
2020-04-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result of 50 epochs in the test data classified accuracy as 100% by using AlexNet (80-20%) and VGG16 (60-40%) architectures and 86.49% in VGG19 (80-20%) architecture.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Illumination is a natural or artificial source and it allows objects to be seen.
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Key Findings & Data:ALOI-COL consists of 1000 classes such as food and toys obtained with 12 different color temperatures.
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Conclusion & Impact:As a result of 50 epochs in the test data classified accuracy as 100% by using AlexNet (80-20%) and VGG16 (60-40%) architectures and 86.49% in VGG19 (80-20%) architecture.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Aydınlatma · Derin Öğrenme Mimarileri · Görüntü İşleme

Abstract

Illumination is a natural or artificial source and it allows objects to be seen. Especially use of illumination for necessary in image processing applications for correct and complete object information captured from images. However type, brightness and position of lighting source change, it also changes the image, color, shadow or size of the object and it causes to appear differently on the object. Therefore, the use of a strong artificial intelligence technique to distinguish images will ease the differentiation of classes. Convolutional Neural Networks (CNN), an artificial intelligence method, is an algorithm that can automatically extract features and easily identify obvious features as learning is provided while training network. In the study ALOI-COL dataset used. ALOI-COL consists of 1000 classes such as food and toys obtained with 12 different color temperatures. Fruit images of 29 classes in the dataset were classified using the CNN architectures AlexNet, VGG16 and VGG19. The images in the dataset were increased with image processing techniques and 51 images of each class created. The study 80-20% and 60-40% training-test examined in two structures. As a result of 50 epochs in the test data classified accuracy as 100% by using AlexNet (80-20%) and VGG16 (60-40%) architectures and 86.49% in VGG19 (80-20%) architecture.

Özet

Aydınlatma, nesnelerin olduğu gibi görünmesini sağlayan doğal veya yapay kaynaklardır. Özellikle görüntü işleme uygulamalarında yakalanan görüntüdeki nesne bilgisinin eksiksiz ve doğru şekilde alınabilmesi için aydınlatmanın kullanılması bir gerekliliktir. Ancak aydınlatma kaynağının tür, parlaklık ve konumunun değişimi; nesnenin görüntüsü, rengi, gölgesi veya boyutunun da değişmesine ve nesnenin farklı olarak algılanmasına sebep olmaktadır. Bu sebeple görüntülerin ayırt edilmesinde güçlü bir yapay zeka tekniğinin kullanılması, sınıfların ayırt edilmesini kolaylaştıracaktır. Bir yapay zeka yöntemi olan Evrişimsel Sinir Ağları (ESA), otomatik olarak özellikleri çıkarabilen ve ağ eğitilirken öğrenme sağlandığı için bariz özellikleri kolaylıkla belirleyen bir algoritmadır. Çalışmada ALOI-COL veriseti kullanılmıştır. ALOI-COL, 12 farklı renk sıcaklığıyla elde edilmiş 1000 sınıftan oluşan bir verisetidir. ALOI-COL verisetindeki 29 sınıftan oluşan meyve görüntüleri, ESA mimarilerinden AlexNet, VGG16 ve VGG19 kullanılarak sınıflandırılmıştır. Verisetindeki görüntüler, görüntü işleme teknikleriyle zenginleştirilmiş ve her sınıftan 51 adet görüntü elde edilmiştir. Çalışma; %80-20 ve %60-40 eğitim-test olmak üzere iki yapıda incelenmiştir. 50 devir çalıştırılması sonucunda test verileri, AlexNet (%80-20) ve VGG16 (%60-40) mimarilerinde %100, VGG19 (%80-20) mimarisinde ise %86.49 doğrulukla sınıflandırılmıştır.

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Cite This Article

Büyükarıkan, B., Ülker, E. (2020). FRUIT CLASSIFICATION WITH CONVOLUTION NEURAL NETWORK MODELS USING ILLUMINATION ATTRIBUTE. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 25 Sayı: 1, 81–100. https://doi.org/10.17482/uumfd.628166

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